A landscape-based approach for delineating hotspots of wildlife-vehicle collisions

Imposing human perceptions about the scales of ecological processes can produce unreliable scientific inferences in wildlife research and possibly misinform mitigation strategies. An example of this disconnect occurs in studies of wildlife-vehicle collisions (WVCs). Subjective procedures are often used to delineate hotspots of WVCs, resulting in hotspots that are not spatially independent. We developed a new approach that identifies independent hotspots using attributes of the landscape to inform delineations instead of subjective measures. First, we generated a candidate set of grouping scenarios using unique combinations of kernel-density estimation parameterization (i.e., bandwidth and isopleth values). Next, we associated the groups of WVCs with attributes of the surrounding landscape. Finally, we identified the grouping scenario with the highest amount of variation in the landscape among the groups. The highest variation corresponded to hotspots that were most distinguishable from each other (i.e., most independent) based on the surrounding landscape. We tested our approach on 3 species of wildlife [island foxes (Urocyon littoralis) on San Clemente Island, CA; white-tailed deer (Odocoileus virginianus) in Onondaga County, NY; and moose (Alces alces) in western Maine] that exemplified varying degrees of space-use in different landscapes. We found that the landscape-based approach was able to effectively delineate independent hotspots for each species without using subjective measures. The landscape-based approach delineated fewer or larger hotspots than currently used methods, suggesting a reduction in spatial dependency among hotspots. Variation in the landscape indicated that hotspots may be larger than previously identified; therefore current mitigation strategies should be adjusted to include larger areas of high risk.